Fault Diagnosis of Belt Conveyor Based on Information Entropy Reconstruction Empirical Mode Decomposition Neural Network
Proposed a neural network model for the early fault detection of belt conveyor motor bearing.By integrating empirical mode decomposition and information entropy,the model reconstructs signals and extracts frequency domain features for neural network training to realize the very accurate fault diagnosis.This method can effectively mitigate noise interference and is highly sensitive to initial fault signals which achieves a diagnostic accuracy of 95.8%.
belt conveyorempirical mode decompositionneural networkbearing fault diagnosis
NETL
NSTL
万方数据
